Dynamic Time-Alignment Kernel in Support Vector Machine
Hiroshi Shimodaira, Ken-ichi Noma, Mitsuru Nakai, Shigeki Sagayama · ERA · 2001
A new class of Support Vector Machine (SVM) that is applica-ble to sequential-pattern recognition such as speech recognition is developed by incorporating an idea of non-linear time alignment into the kernel function. Since the time-alignment operation of sequential pattern is embedded in the new kernel function, stan-dard SVM training and classication algorithms can be employed without further modications. The proposed SVM (DTAK-SVM) is evaluated in speaker-dependent speech recognition experiments of hand-segmented phoneme recognition. Preliminary experimen-tal results show comparable recognition performance with hidden Markov models (HMMs). 1